Build
AI agents, workflows, internal applications and AI-powered interfaces, without rebuilding common infrastructure every time.
Enterprise AI, without vendor lock-in
Own your AI.
Don’t rent it.
स्वावलंबी (Svāvalambī): self-supporting
Svalamba is the enterprise AI platform for building, deploying and governing every AI application i.e. agents, workflows and interfaces; on any model, in any environment, from one place.
The problem
Enterprise AI is fragmenting.
Organizations buy a different AI tool for every team i.e. meeting assistants, enterprise search, coding copilots, document AI, support bots. As adoption grows, companies end up managing dozens of disconnected AI systems instead of one governed platform. Every additional product introduces the same overhead:
Another subscription
Another integration
Another security review
Another data silo
Another vendor dependency
The platform
One platform for every enterprise AI application.
Connect
Reusable enterprise connectors for Microsoft, Google Workspace, AWS, Azure, Jira, Confluence, PostgreSQL, vector databases and internal APIs.
Deploy
Ship the same application to cloud, private cloud, on-premise servers or air-gapped infrastructure, no architecture changes.
Operate
Users, permissions, knowledge, models, audit logs, compliance and observability, managed from a single dashboard.
Multi-model runtime: switch providers without rebuilding
- OpenAI
- Anthropic
- DeepSeek
- Llama
- Mistral
Where Svalamba sits
The market has split into tools without outcomes and outcomes without neutrality. Svalamba is the only column with a full house.
| Capability | AI gateways | Hyperscaler FDE | Data platforms | Svalamba |
|---|---|---|---|---|
| Self-hosted, data stays yours | Yes | No | No | Yes |
| Embedded deployment support | No | Yes | Yes | Yes |
| Token & cost optimization | Yes | No | No | Yes |
| Multi-region compliance | No | No | Yes | Yes |
| Model & vendor neutrality | Yes | No | No | Yes |
Why now: the market
The market is at an inflection point.
- $37B
- Enterprise GenAI spend in 2025, up 3.2x in one year [1]
- $2.5M → $12.3M
- Average enterprise LLM spend, 2024 to 2026 [2]
- 37%
- of CIOs already run five or more AI models [2]
- 95%
- of GenAI pilots fail to deliver measurable ROI [3]
- $1.5B
- Healthcare AI spend in 2025: 43% of all vertical AI [1]
Betting on one vendor is a coin flip
Frontier-model share of enterprise LLM usage shifted violently in just two years. Applications welded to one provider inherit that volatility.
Source: Menlo Ventures, 2025 [1]
AI budgets are compounding
Average enterprise LLM spend is on track to grow five-fold in three years.
Source: Andreessen Horowitz, 2025 [2]
The operations layer is exploding
The global MLOps market, the tooling that runs production AI, is forecast to grow 30x within a decade.
Source: MLOps market sizing aggregates, 2025 [7]
A large and reachable market
Top-down market pools and a bottom-up obtainable path converge on the same opportunity.
$15–20B (2026) → $130–150B (2034)
MLOps + LLM APIs + multi-cloud management + AI services
$5–15B annually
50–100K companies spending >$120K/yr on AI APIs in US, EU & India
$50–125M ARR by Year 3
300–500 customers at $100–250K ACV
Obtainable ARR path (high case, $M)
Source: Svalamba market research, 2026 [8]
The platform layer is becoming standard
Gartner projects a 14x rise in AI-gateway adoption among organizations building multi-LLM applications.
Source: Gartner, 2025 [4]
Why now: the economics
Most AI spend is recoverable.
Agents multiply token bills
Agentic workflows consume many multiples of a simple chat query; unmanaged, spend scales with ambition.
Source: Industry benchmarks, 2025 [6]
Optimization recovers most of it
Published optimization levers, applied together, cut LLM costs by well over half.
Source: Provider pricing documentation & industry benchmarks, 2025 [5]
Caching discounts differ by provider
Cached input tokens are discounted very differently; routing across providers is a cost lever in itself.
Source: Provider pricing documentation & industry benchmarks, 2025 [5]
Where enterprise AI budgets go
Directional split (midpoints of published ranges): tooling and compute dominate, governance is chronically underfunded.
- AI SaaS & tools ~35%
- Cloud & compute ~22%
- Talent ~17%
- Implementation ~13%
- Governance ~10%
Source: Enterprise AI budget surveys, 2025 [10]
…and switching later is expensive
The true cost of a vendor relationship is never the sticker price; it’s the exit price.
Deployment
Build once.
Deploy anywhere.
Healthcare, banking, government and defense often cannot send sensitive data to third-party AI providers. Svalamba runs where your data lives: the same application architecture across all four environments.
Public Cloud
Private Cloud
On-Premise
Air-Gapped
Core principles
“AI should be infrastructure, not another SaaS subscription.”
- Enterprises should own their AI instead of renting it.
- AI applications should be portable across models and deployment environments.
- Compliance should be built into the platform, not added later.
- Organizations should build once and deploy anywhere.
- Every enterprise AI application should share the same reusable platform.
Contact
Talk to us.
We are onboarding early design partners. Tell us what your teams are building, and what it currently costs you to rent it.
[email protected]Legal
Terms of Use
Last updated: July 22, 2026
Placeholder template content, not legal advice. Consult qualified counsel before relying on this document.
1. Acceptance of terms
By accessing or using the Svalamba website, platform, or any related services (the “Services”), you agree to be bound by these Terms of Use. If you do not agree, do not use the Services.
2. Description of service
Svalamba provides an enterprise AI platform for building, deploying, and governing AI applications (agents, workflows, and user interfaces) across cloud, private cloud, on-premise, and air-gapped environments.
3. Acceptable use
You agree not to use the Services to: (a) violate any applicable law or regulation; (b) infringe the intellectual property or privacy rights of any third party; (c) transmit malware or other harmful code; (d) attempt to gain unauthorized access to any systems or data; or (e) interfere with the operation or security of the Services.
4. Intellectual property
The Services, including all software, content, trademarks, and branding, are owned by Svalamba or its licensors. Customer data and customer applications built on the platform remain the property of the customer.
5. Disclaimers & limitation of liability
The Services are provided “as is” and “as available” without warranties of any kind. To the maximum extent permitted by law, Svalamba shall not be liable for any indirect, incidental, special, consequential, or punitive damages arising out of your use of the Services.
6. Changes & governing law
We may update these terms from time to time; continued use constitutes acceptance. These terms are governed by applicable law in the jurisdiction where Svalamba is established. Questions: [email protected].
Privacy Policy
Last updated: July 22, 2026
Placeholder template content, not legal advice. Consult qualified counsel before relying on this document.
1. Information we collect
We collect information you provide directly (such as your name and email when you contact us) and limited technical information generated by your use of this website. The Svalamba platform is designed so that customer data can remain within customer-controlled infrastructure.
2. How we use and share information
We use collected information to respond to inquiries, provide and improve the Services, maintain security, and comply with legal obligations. We do not sell personal information, and we share it only with service providers under confidentiality obligations, when required by law, or in connection with a corporate transaction.
3. Retention & security
We retain personal information only as long as necessary, then delete or anonymize it. We apply administrative, technical, and physical safeguards appropriate to the sensitivity of the information, including encryption in transit and access controls.
4. Your rights
Depending on your jurisdiction (including GDPR and CCPA-style regimes), you may have rights to access, correct, delete, or port your personal information, and to object to or restrict certain processing. Privacy inquiries: [email protected].
IP Infringement Policy
Last updated: July 22, 2026
Placeholder template content, not legal advice. Consult qualified counsel before relying on this document.
1. Our commitment
Svalamba respects the intellectual property rights of others and responds to clear notices of alleged infringement consistent with applicable law, including DMCA-style procedures.
2. Reporting alleged infringement
If you believe content available through the Services infringes your rights, send a written notice including:
- Identification of the protected work claimed to be infringed;
- Identification and location of the allegedly infringing material;
- Your name, address, email address, and telephone number;
- A good-faith-belief statement that the disputed use is not authorized;
- A statement, under penalty of perjury, that your notice is accurate and that you are the rights owner or authorized to act on the owner’s behalf;
- Your physical or electronic signature.
3. Counter-notice & repeat infringers
If your material was removed in error, you may submit a counter-notice with your contact details, identification of the removed material, a statement under penalty of perjury, and consent to the jurisdiction of the applicable courts. We may suspend or terminate access for repeat infringers.
4. Designated contact
Send notices to [email protected] with the subject line “IP Infringement Notice”.
Data Compliance
Last updated: July 22, 2026
Placeholder template content, not legal advice. Consult qualified counsel before relying on this document.
1. Deployment models & data residency
Svalamba is designed so organizations choose where their data lives. The platform deploys to public cloud, private cloud, on-premise, and air-gapped infrastructure without changes to application architecture. In self-hosted deployments, customer data, prompts, and model traffic remain entirely within customer-controlled infrastructure.
2. Encryption, access controls & audit logs
Data is encrypted in transit using modern TLS; at rest, encryption follows industry standards (managed deployments) or the customer’s own key management (self-hosted). Role-based access controls, permission management, and audit logging are first-class platform capabilities.
3. Regulatory alignment
Svalamba is built with the requirements of regulated industries (healthcare, banking, insurance, government, and defense) as design inputs. Alignment with frameworks such as SOC 2, GDPR, and HIPAA is part of our engineering roadmap. These statements describe design intent, not certification claims; certification status will be published as audits complete.
4. Subprocessors & contact
A list of subprocessors used in managed deployments will be published and maintained. Self-hosted and air-gapped deployments involve no Svalamba subprocessors. Compliance inquiries: [email protected].
Sources
- [1] Menlo Ventures, “2025: The State of Generative AI in the Enterprise” (2025) · menlovc.com
- [2] Andreessen Horowitz, “AI Enterprise 2025: How 100 CIOs Are Building and Buying Gen AI” (2025) · a16z.com
- [3] MIT, “The GenAI Divide: State of AI in Business” (2025)
- [4] Gartner, “AI gateway adoption forecast for multi-LLM enterprise applications, 2024–2028” (2025)
- [5] Provider pricing documentation & industry benchmarks, “Anthropic / OpenAI prompt-cache pricing; published token-optimization case studies” (2025)
- [6] Industry benchmarks, “Token consumption of agentic workloads vs. single-pass chat queries” (2025)
- [7] MLOps market sizing aggregates, “Global MLOps market forecasts, 2025–2034” (2025)
- [8] Svalamba market research, “TAM/SAM/SOM analysis across MLOps, LLM API, multi-cloud and AI services spend pools” (2026)
- [9] Enterprise IT migration surveys, “Platform migration cost benchmarks” (2025)
- [10] Enterprise AI budget surveys, “Enterprise AI budget allocation (directional midpoints of published ranges)” (2025)
* Figures reflect third-party research; ranges are as published by the cited sources. Items marked directional are midpoints of published ranges.